Fatigue driving is a major cause of traffic accidents, and excessive cognitive load significantly exacerbates driver fatigue. This study integrates cognitive load theory with multimodal physiological signals (e.g., eye tracking, EEG, HRV) and behavioral data to develop a dynamic fatigue assessment model and optimize the human-machine interaction design of in-vehicle warning systems. Through driving simulation experiments and real-world tests, we examine the impact of different cognitive tasks (e.g., complex road conditions, multitasking) on driver fatigue and propose an adaptive warning strategy based on cognitive load levels. The results demonstrate that a cognitive-state-aware warning system reduces false alarms and improves drivers’ response efficiency. This research provides new insights for applying human factors engineering to intelligent driving safety.